The rising average life expectancy and higher ratio of seniors in the population increased the prevalence of chronic diseases. Long term medical care is required for larger proportion of such subjects, besides decreasing the length of stay in hospitals. Preventive health care providing patient monitoring at home or residence, reducing the readmission rates at the hospital, has been a major motivational factor for personal health monitoring in line with patient monitoring.
Mobile medical sensors provide an efficient, accurate and economical means to monitor the health of the subject outside the hospital. There has been a tremendous increase in the number of wearable devices employing mobile medical sensors to monitor the health of the subject, and has gained importance in personal health care monitoring and its system.
Currently, the wearable device have the capability to collect single lead ECG signal and continuously store the data and upload the same into the cloud for further analysis. It becomes very useful to identify the state or condition of the subject at the time of obtaining the ECG. In other words, identifying the activity that been performed by the subject while obtaining ECG provides a greater insight into the understanding and analyzing the ECG data.
ECG data along with the activity information becomes very helpful in providing proper diagnosis to the subject, wherein such diagnosis relies upon the ECG data. Accordingly, current technology employs accelerometer to detect the activity of the subject when the ECG is being obtained. The activity information is stored along with the ECG. Since the ECG data and the activity information using the accelerometer are obtained independent of each other, mapping of these two data/information is required. Also, not all the activities of the subject can be detected correctly. The accelerometer detects and measures only the motion and not exertion no matter how hard the subject is straining or the deadlift is. For instance, the motion detected by the accelerometer from eating seems more vigorous than the bicep curls. Also, the activities such as sleep, rest or sitting idle cannot be differentiated properly. Due to this set back, mapping the activity with the ECG data is not robust and has reduced reliability.
US20110245688 A1 requires ECG signal along with a motion detection feature to identify the activity of the person. Here, ECG and a plurality of sensors are communicatively coupled along with motion detection feature. Real time synchronization of ECG data and motion data needs to be dealt with carefully.
US20080300641A1 deals with generating cardiac information descriptive of cardiac functioning of a patient, detecting a cardiac anomaly based on an analysis of the cardiac information. It also discloses generating activity information descriptive of physical activity of the patient during the cardiac anomaly, and associating the cardiac information and the activity information, during the cardiac anomaly.
It is well appreciated that the ECG do provide a lot of information regarding the health of the subject. However, for more accurate diagnosis the activity being performed by the subject when the ECG was recorded need to be known along with the ECG. The invention is aimed at overcoming the problems associated with mapping of the ECG data and the activity information obtained independently. The invention proposes to auto label the ECG data with the activity information by deciphering only the ECG signal.